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Med Sci Monit. 2017; 23: 1069–1082.
Published online 2017 February 28. doi:  10.12659/MSM.899716
PMCID: PMC5341907

Patients with Obstructive Sleep Apnea Display Decreased Flow-Mediated Dilatation: Evidence from a Meta-Analysis

Yuyu Wang,1,2,3,A,B,C,D,E,F,* Huajun Xu,1,2,B,C,D,* Yingjun Qian,1,2,B,F Jian Guan,1,2,A,E Hongliang Yi,1,2,A,E and Shankai Yin1,2,A



Endothelial dysfunction, which can be measured by flow-mediated dilatation (FMD), is an early clinical marker of atherosclerosis, which is considered to be the main cause of the observed cardiovascular complications in obstructive sleep apnea (OSA) patients. The association between OSA and endothelial dysfunction has been reported in a number of studies; however, the findings are not entirely consistent. Our aim was to meta-analytically synthesize the existing evidence to explore the association between OSA and endothelial dysfunction.


Data from PubMed, EMBASE, the Cochrane library, and Google Scholar for all trials that investigated the relationship between endothelial dysfunction and OSA were systematically reviewed. The minimum inclusion criteria for the studies were reporting of the Apnea-Hypopnea Index (AHI) and FMD measurements (as an indicator of endothelial dysfunction) for both OSA and control groups. Data from case-control studies that met the inclusion criteria were extracted.


Twenty-eight studies comprising a total of 1496 OSA patients and 1135 controls were included in the meta-analysis. A random-effects model was used. The weighted mean difference in the FMD measurements was −3.07 and the 95% confidence interval was −3.71 to −2.43 (P<0.01). Meta-regression analysis showed that age, sex, body mass index (BMI), blood pressure, glucose, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol did not explain the heterogeneity.


This meta-analysis showed that patients with OSA have decreased FMD, which may contribute to the development of atherosclerosis.

MeSH Keywords: Sleep Apnea, Obstructive; Meta-Analysis as Topic; Atherosclerosis


Obstructive sleep apnea (OSA) is a chronic disorder characterized by repetitive apneas, oxygen desaturation, and disruption during sleep [13]. OSA affects 3%–24% of the general population and an even higher percentage (35–45%) of individuals who are obese or are suffering from diabetes mellitus (DM) [47]. Recent studies have found that OSA increases the risk of cardiovascular disease (CVD) independent of age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), or smoking habit [810]. Importantly, atherosclerosis is considered to be the main cause of cardiovascular complications in OSA patients [11].

Endothelial dysfunction, as an early marker of atherosclerosis, correlates significantly with OSA [12]. In general, the multiple methods measuring markers of endothelial function were invasive procedures. Recently, flow-mediated dilatation (FMD) [13], a non-invasive method that evaluates nitric oxide (NO)-dependent vasodilatation, has been used to detect atherosclerosis in its subclinical phase. As a safe and convenient procedure, FMD is of interest for large-scale screening for endothelial dysfunction. However, studies examining the relationship between OSA and FMD have reported conflicting results. Therefore, we performed this meta-analysis to assess whether atherosclerosis could be detected based on brachial artery FMD in patients with OSA.

Material and Methods

The meta-analysis was performed in accordance with the recommendations of the Cochrane Collaboration and the Quality of Reporting of Meta-analyses (QUORUM) guidelines.

Data collection

Studies reported in English concerning OSA and endothelial function were identified by searching electronic databases, including PubMed, EMBASE, the Cochrane library, and Google Scholar. The databases were searched from the earliest available dates until May 16, 2016. Only those papers published with full-length text were considered. Unpublished data from scientific meetings were also searched but not included since their abstracts did not provide detailed data. The search terms used were “obstructive sleep apnea” or “sleep apnea” or “OSA” or “sleep-breathing disorders” and “flow-mediated” or “FMD” or “endothelial function” or “endothelial dysfunction” or “endothelium-dependent.” References for all relevant articles, review articles, and relevant non-electronic literature were searched manually to identify additional relevant studies. Two authors (Drs. Wang and Xu) individually searched and scored manuscripts for inclusion. Manuscripts were scored in duplicate, and if their scores differed, a third author (Dr. Guan) participated and inclusion was decided through discussion.

Inclusion and exclusion criteria

FMD was selected as a marker of endothelial function based on a review of the literature. Studies were included if they met the following criteria: (1) they were published in English and performed on adult humans; (2) full-text manuscripts were available; (3) they included at least two separate groups, one diagnosed with OSA and the other made up of control subjects without OSA; (4) OSA was diagnosed by polysomnography (PSG); (5) initial FMD values recorded by ultrasound were available; and (6) the reported values were presented as means and standard deviation or standard error or interquartile range. Studies were excluded from the analysis for the following reasons: (1) FMD was not used to measure endothelial function; (2) the results of comparison were not reported, or the data could not be extracted from the published results; (3) they were non-human studies, letters, reviews, or case reports; (4) the patients had other medical conditions that may have interfered with sleep, such as chronic respiratory disorders, heart failure, or uncontrolled allergies, or they were being treated with continuous positive airway pressure (CPAP); (5) the outcomes of the same patient group were reported in another publication (in that case, the higher-quality article was included); (6) the trials were not published in English. The definition of OSA varied in the different publications, because Apnea-Hypopnea Index (AHI) cutoffs in epidemiological investigations conducted to date have been variable. Thus, our meta-analysis also accepted OSA as defined by the authors and not by the AHI criteria.

Data extraction

Two reviewers (Dr. Wang and Xu) independently assessed the content of all studies to be included, and selected those that met the inclusion criteria while annotating the reasons for study exclusion. Included studies were carefully scanned and the following information was extracted: first author, year of publication, number of participants, subject demographics (age, sex, and BMI), AHI, FMD values, and confounding factors (SBP, DBP, high-density lipoprotein [HDL] cholesterol, low-density lipoprotein [LDL] cholesterol, and glucose).

For studies in which OSA groups were divided based on severity, all sets of data were combined into one single group. For example, Chami and colleagues [14] divided OSA patients into mild, moderate, and severe groups, and the control group into two groups (AHI 1.5–4.9, AHI <1.5). Therefore, we combined the three OSA groups as well as the two control groups into one OSA and one control group according to the methodology of the Cochrane Handbook (9.2.4 Effect measures for ordinal outcomes and measurement scales). If the study enrolled participants with other diseases, then the methodology was processed in the same way.

Statistical analysis

Continuous values such as FMD were analyzed using the weighted mean difference (WMD) and were reported with 95% confidence intervals (CIs). The WMD summarizes the differences between two groups with respect to continuous variables, while accounting for sample size. For studies that presented continuous data as means and SEs, the SDs were calculated using the formula SD=SE×√n. For studies that presented continuous data as means and quartiles, the SDs were calculated using statistical algorithms according to the Cochrane Handbook: SD=v73-v231.35. We graphically inspected Forest plots and used I2 statistics to evaluate heterogeneity. An I2 of 25–49% was considered to represent a low level of heterogeneity, 50–74% a moderate level, and 75–100% a high level. A fixed-effects or random-effects model was used according to the heterogeneity between studies. Meta-regression and subgroup analysis were conducted to reveal potential sources of heterogeneity. The covariates in the regression analyses included age, sex, BMI, blood pressure, glucose, and HDL and LDL cholesterol. Subgroup analysis was used to assess the heterogeneity of race. Egger’s test and funnel plots were used to test publication bias. The powers of the included studies were determined using Power and Precision V4 software. All meta-analyses were conducted using the Cochrane Collaboration’s Review Manager Software version 5.0 and STATA version 12.0 (StataCorp., College Station, Texas, USA) software packages.


Literature search

We identified 713 titles of potentially relevant articles from the literature search. From these, 622 articles were excluded based on preliminary title and abstract screening (irrelevant=361, case report or review=191, non-adult population=44, non-English=26). The remaining 91 studies were scanned for full-text evaluation, and a further 63 articles were excluded for the following reasons: 31 articles did not use FMD to measure endothelial function, 11 did not contain original data, 16 did not have a control group, 3 included OSA patients under CPAP treatment, and 2 used medians with a range (minimum, maximum) to measure FMD (Figure 1). Thus, 28 articles covering 1496 OSA patients and 1135 controls were finally included in the meta-analysis [11,12,1439].

Figure 1
Flow diagram of included and excluded studies.

Study characteristics

A total of 28 articles providing 28 data sets were pooled for this meta-analysis. In these case-control studies, 11 were from Asian researchers, 10 from American researchers, and 7 recruited patients from European countries. A total of 24 were clinically based, recruiting patients from hospitals or research centers, while 4 were population-based. Most studies used in-hospital complete PSG. There were no significant differences between the methods to measure FMD. The outcomes of the studies are discussed in Table 1.

Table 1
Characteristics of the included case-control studies on OSAS and FMD.

Meta analysis of studies on OSA and FMD

The pooled data from the eligible studies suggested that FMD was significantly reduced in OSA patients. For FMD, the WMD was −3.07 and the 95% CI was −3.71 to −2.43 (P<0.00001). We re-performed another meta-analysis of studies using only full PSG to exclude the impact of portable monitoring devices. The relationship between OSA and FMD remained the same (WMD: −3.30, 95% CI: −3.83 to −2.78, P<0.00001). Significant heterogeneity was observed between the studies in both analyses (I2=90%, I2=78%). Thus, a random-effects model was applied (Figures 2, ,33).

Figure 2
Forest plot summarizing the results of the random-effects meta-analysis of the association between OSA and FMD.
Figure 3
Forest plot summarizing the relationship between OSA and FMD within articles using only full PSG to diagnose OSA.

In addition, when studies with an inadequate number of OSA subjects (<20 in each group) [11,12,16,19,2530,3234,39] were excluded, according to the guidelines for the measurement of FMD [40], the pooled data also provided a robust result (WMD: −2.42, 95% CI: −3.32. to −1.52, P<0.001) (Supplementary Figure 1).

A power calculation showed that seven studies [14,16,21,23, 24,38,39] lacked sufficient power (<80%) (Table 2). However, their exclusion did not change the pooled result (WMD: −3.50, 95% CI: −4.04. to −2.96, P<0.001) (Supplementary Figure 2).

Table 2
Power calculation of all included articles.

Meta-regression analysis, subgroup analysis, and publication bias

Multiple meta-regression analyses were performed to evaluate the effect of the covariant variables on FMD when reported. The confounding factors were recruited from the articles in which they were mentioned. Age (slope=0.149, P=0.136), sex (slope=6.970, P=0.095), BMI (slope=−0.014, P=0.817), SBP (slope=0.193, P=0.147), DBP (slope=0.204, P=0.080), glucose (slope=−0.058, P=0.486), triglycerides (slope=0.017, P=0.592), total cholesterol (slope=0.028, P=0.607), HDL cholesterol (slope=0.091, P=0.280) and LDL cholesterol (slope=−0.167, P=0.066) were shown to not have a significant effect as confounding factors (Table 3).

Table 3
Meta-regression of all confounding factors.

To study the effect of complications, such as hypertension, diabetes, dyslipidemia, and treatment, we conducted a subgroup analysis according to whether the recruited subjects were free of the above disorders. The result showed that there was a significant association between OSA and decreased FMD in subjects with (WMD: −2.83, 95% CI: −3.67 to −1.98, P<0.001) versus those without (WMD: −3.51, 95% CI: −4.41 to −2.60, P<0.001) these disorders (Supplementary Figure 3).

Another subgroup analysis was conducted trying to explain the heterogeneity. The pooled analysis was divided into three subgroups according to continent. Thus, 11 studies were from Asia, 10 were from the USA, and 7 were from Europe. In all of the subgroups, decreased FMD was related to OSA: the WMD (95% CI) values from each subgroup were −2.68 (−2.96 to −2.40), −1.46 (−1.76 to −1.16), and −2.54 (−3.00 to −2.09), respectively. The I2 values from the three subgroups were 88%, 92%, and 75%, respectively (Asia, America, and Europe). In the European subgroup, the heterogeneity was no longer apparent (I2=0%) (WMD: −3.03, 95% CI: −3.55. to −2.51) after the two studies that recruited subjects from Italy were excluded (Figures 4, ,55).

Figure 4
Meta-analysis of the relationship between OSA and FMD according to the geographical location of the patients.
Figure 5
Meta-analysis of the relationship between OSA and FMD in Europe (excluding studies recruiting subjects from Italy).

Egger’s test (P=0.249) and funnel plot (Figure 6) showed no evidence of publication bias.

Figure 6
Begg’s funnel plot.


To the best of our knowledge, this is the first meta-analysis to pool the available data and provide a summary of the relationship between decreased FMD and OSA patients. Twenty-eight studies, pooling 1496 OSA patients and 1135 controls, were included. FMD was found to be significantly lower in OSA patients than in the controls.

Though many previous studies have assessed the relationship between OSA and endothelial dysfunction, the results are conflicting. The majority of studies reviewed for this analysis reported a decrease in FMD in OSA patients compared with controls. After summarizing all of the data, our results showed a statistically significantly lower FMD in OSA patients than in controls. In addition, the results of several studies support a correlation between OSA and decreased FMD, while also demonstrating that FMD values become even smaller as the severity of OSA increases. In those studies, the subjects were divided into control, mild, moderate, and severe OSA groups [2124,35], and the results showed that moderate-severe OSA patients suffered more from decreased FMD compared with mild OSA patients. However, it is regrettable that the validity of this relationship remains to be established due to the small number of relevant studies; thus, large-scale studies are required to obtain high-level evidence.

The mechanism underlying OSA impairment of endothelial function is unclear but is likely to involve several pathways. The three acute consequences of OSA, intermittent hypoxia, intra-pleural pressure swings, and recurrent arousals, are thought to be the main causes of impaired endothelial function [4143]. Of these, intermittent hypoxia is considered the most important factor promoting the production of reactive oxygen species (ROS), thereby increasing oxidative stress and decreasing nitric oxide (NO) synthetase activity. This causes an attenuation of NO and an impairment of endothelial function [43,44].

Whether OSA is independently associated with decreased FMD is controversial. Some studies [21,31,33,39] have reported that age, BMI, and SBP were correlated with FMD; in contrast, others [11,19,36] found no significant relationship between FMD and age, BMI, SBP, DBP, lipids, or fasting glucose levels. Sex may be another confounding factor. Faulx et al. [24] showed an association between moderate OSA and impaired endothelial function in females. Females were also more vulnerable than males to early OSA-related cardiovascular diseases. After adjustment for variables significantly associated with FMD, Namtvedt et al. [31] and Jelic et al. [26] reported an independent association between increasing AHI and a reduction in FMD. According to Chami et al. [14], however, there was no apparent association between OSA and FMD after adjustment for age, sex, race, and all covariates. Since the data in the majority of included studies were not adjusted for covariates, significant findings about the impact of confounding factors cannot be obtained.

Heterogeneity was observed in our meta-analysis; meta-regression and subgroup analysis were conducted to determine the potential sources of the heterogeneity. Meta-regression analysis excluded age, sex, BMI, blood pressure, glucose, HDL cholesterol, and LDL cholesterol as sources of heterogeneity. Subgroup analysis suggested that ethnicity explained at least part of the heterogeneity. The European subgroup exhibited an I2 of 75%; however, the exclusion of two studies that recruited subjects from Italy lessened the heterogeneity (I2=0%). We assumed that this was due to ethnic diversity among the subgroups, leading to different physiological responses. Other factors may also have contributed to the heterogeneity. The majority of studies included were clinically rather than community based, which could have introduced referral bias, resulting in heterogeneity. Also, FMD might be affected by environmental effects such as noise, temperature, alcohol, caffeine, or fasting. Although these factors were well controlled in the majority of studies according to the methods described by Celermajer et al. [45], variations among studies are possible.

Multiple limitations in this meta-analysis should be addressed. First, the included studies were limited to publications in English, which may have increased the possibility of publication bias. In addition, it is known that positive results are more likely to be published; since we included data only from published studies, publication bias was likely. Second, no randomized controlled trials and no prospective studies were identified. Third, this meta-analysis was not an overview of all methods of evaluating endothelial dysfunction. Other indicators of endothelial function, such as NO levels, endothelin-1 (ET-1) levels, measurements of circulating endothelial cells (CECs), and peripheral artery tonometry (PAT), were not searched for and evaluated. Also, most included articles did not clearly describe their OSA patients, such as with respect to compliance and complications, both of which may affect the results. Nonetheless, the subgroup analysis showed that decreased FMD was related to OSA in all subgroups. Recently, several articles [31,46] declared that FMD is concomitantly dependent on initial artery diameter, which may itself be higher in OSA patients. Thus, future studies should carefully consider initial artery diameter. Other limitations pertaining to the methods of the individual studies included in this meta-analysis should also be addressed. Finally, some of the included studies lacked enough power to detect an association or were not based on the use of standard PSG to diagnose OSA, but our results were also robust when these inadequate studies were excluded.


OSA significantly decreases FMD in OSA patients compared with controls. Future larger randomized studies of longer duration should focus on the effect of treatment of OSA on endothelial dysfunction.

Supplemenatry Figures

Supplementary Figure 1.

Forest plot excluding studies with an inadequate number of OSA subjects (<20 in each group).

Supplementary Figure 2.

Forest plot excluding studies lacked sufficient power (<80%).

Supplementary Figure 3.

Meta-analysis of the relationship between OSA and FMD according to whether other disorders were excluded or not.


Source of support: Departmental sources


The investigators have no financial associations with any entity with an interest in the subject of this study. There was no funding from any institution.


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